Prior to the advent of the personal computer, a central computer, for example, a mainframe or mint-computer, supporting multiple individual user terminals was the dominant computing paradigm. This centralized architecture allowed for efficient use of expensive computer hardware and software resources by sharing those resources across multiple users.
With the arrival of the relatively inexpensive personal computer, a new computing model arose that employed computing resources localized at each user to displace the remote and centralized resources typifying the earlier era.
The advantages of the centralized computing model are many however, and ubiquitous networking has led to the resurgence of centralized computing in a variety of forms. In one embodiment, a server provides processing and/or storage resources to one or more remote clients. In other embodiments, a remote client is assigned exclusive access to a workstation or personal computer module maintained at a central site. In centralized systems, consolidation of data and program storage and/or computing resources reduces maintenance and support costs, while enabling increased system security. The remote client that accesses a centralized system may take various forms. A remote client having minimal processing and storage capabilities, and that relies on centralized resources for adequate functionality is known as a “thin client.” Similarly, a remote client possessing more extensive processing and storage capabilities is termed a “fat client” or a “thick client.”
Centralized computer systems are not without disadvantages. For example, failure of a single server supporting multiple users can result in a loss of productivity proportional to the number of users supported. Furthermore, some applications, for example, rendering video on a server and transmitting the renderings to a client for display, can consume an excessive amount of server computing resources and/or network bandwidth. Information, such as video, transmitted from server to client is often highly compressed to reduce the bandwidth required to perform the transfer. Such highly compressed video or other content often results in a less than desirable user experience. Moreover, producing compute intensive content for more than one client can be extremely burdensome for the server, and transmission of the multiple content streams results in an onerous consumption of network bandwidth. Accordingly, improved methods of distributing resources in remote computing systems are desirable.